Overview

Dataset statistics

Number of variables14
Number of observations525
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.5 KiB
Average record size in memory120.0 B

Variable types

NUM10
CAT4

Reproduction

Analysis started2021-03-21 13:44:01.672070
Analysis finished2021-03-21 13:44:37.700134
Duration36.03 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Loan_Purpose has 12 (2.3%) zeros Zeros

Variables

Loan_Amount
Real number (ℝ≥0)

Distinct count145
Unique (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11641.142857142857
Minimum1000.0
Maximum35000.0
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-03-21T19:14:37.741067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3000
Q16600
median10000
Q315000
95-th percentile25000
Maximum35000
Range34000
Interquartile range (IQR)8400

Descriptive statistics

Standard deviation6929.03764
Coefficient of variation (CV)0.595219707
Kurtosis1.196164829
Mean11641.14286
Median Absolute Deviation (MAD)4000
Skewness1.118736261
Sum6111600
Variance48011562.62
2021-03-21T19:14:37.837910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10000509.5%
 
12000479.0%
 
8000244.6%
 
15000214.0%
 
6000203.8%
 
5000183.4%
 
7000152.9%
 
20000152.9%
 
4000142.7%
 
18000112.1%
 
Other values (135)29055.2%
 
ValueCountFrequency (%) 
100020.4%
 
140010.2%
 
150020.4%
 
170010.2%
 
180010.2%
 
ValueCountFrequency (%) 
3500081.5%
 
3342510.2%
 
3182510.2%
 
3000020.4%
 
2800071.3%
 

Term
Categorical

Distinct count2
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
36
438
60
 
87
ValueCountFrequency (%) 
3643883.4%
 
608716.6%
 
2021-03-21T19:14:37.954910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Interest_Rate
Real number (ℝ≥0)

Distinct count29
Unique (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.343171369285846
Minimum6.03
Maximum21.67
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-03-21T19:14:38.044910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6.03
5-th percentile6.62
Q19.91
median12.42
Q314.65
95-th percentile19.03
Maximum21.67
Range15.64
Interquartile range (IQR)4.74

Descriptive statistics

Standard deviation3.712685564
Coefficient of variation (CV)0.3007886266
Kurtosis-0.569513397
Mean12.34317137
Median Absolute Deviation (MAD)2.51
Skewness0.2798869845
Sum6480.164969
Variance13.7840341
2021-03-21T19:14:38.136945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11.71499.3%
 
12.42468.8%
 
9.91428.0%
 
7.9397.4%
 
12.69336.3%
 
10.65326.1%
 
13.49305.7%
 
8.9275.1%
 
7.51214.0%
 
14.65203.8%
 
Other values (19)18635.4%
 
ValueCountFrequency (%) 
6.03193.6%
 
6.62193.6%
 
7.51214.0%
 
7.9397.4%
 
8.9275.1%
 
ValueCountFrequency (%) 
21.6720.4%
 
21.2840.8%
 
20.8920.4%
 
20.340.8%
 
20.0249688810.2%
 

Employment_Years
Real number (ℝ≥0)

Distinct count19
Unique (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.284646470293333
Minimum0.5
Maximum10.0
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-03-21T19:14:38.232998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q12
median5
Q39
95-th percentile10
Maximum10
Range9.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.373365671
Coefficient of variation (CV)0.6383332717
Kurtosis-1.381835628
Mean5.28464647
Median Absolute Deviation (MAD)3
Skewness0.176229441
Sum2774.439397
Variance11.37959595
2021-03-21T19:14:38.314153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1012123.0%
 
26011.4%
 
55410.3%
 
3529.9%
 
1458.6%
 
4448.4%
 
0.5407.6%
 
6346.5%
 
7254.8%
 
8224.2%
 
Other values (9)285.3%
 
ValueCountFrequency (%) 
0.5407.6%
 
1458.6%
 
26011.4%
 
3529.9%
 
4448.4%
 
ValueCountFrequency (%) 
1012123.0%
 
9203.8%
 
8224.2%
 
7254.8%
 
6.53961811810.2%
 

Home_Ownership
Categorical

Distinct count3
Unique (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2
314
0
169
1
 
42
ValueCountFrequency (%) 
231459.8%
 
016932.2%
 
1428.0%
 
2021-03-21T19:14:38.419377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Annual_Income
Real number (ℝ≥0)

Distinct count188
Unique (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57183.21710171768
Minimum12000.0
Maximum126000.0
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-03-21T19:14:38.507281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile26016
Q140000
median52800
Q372800
95-th percentile100400
Maximum126000
Range114000
Interquartile range (IQR)32800

Descriptive statistics

Standard deviation23418.98416
Coefficient of variation (CV)0.4095429629
Kurtosis0.009479602472
Mean57183.2171
Median Absolute Deviation (MAD)16800
Skewness0.6703914849
Sum30021188.98
Variance548448819.1
2021-03-21T19:14:38.589855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
50000234.4%
 
75000224.2%
 
40000214.0%
 
30000203.8%
 
60000183.4%
 
80000163.0%
 
70000152.9%
 
35000142.7%
 
42000132.5%
 
65000122.3%
 
Other values (178)35166.9%
 
ValueCountFrequency (%) 
1200010.2%
 
1225210.2%
 
1500030.6%
 
1700010.2%
 
1710810.2%
 
ValueCountFrequency (%) 
12600010.2%
 
12500010.2%
 
12000081.5%
 
11800010.2%
 
11640010.2%
 
Distinct count3
Unique (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2
218
0
173
1
134
ValueCountFrequency (%) 
221841.5%
 
017333.0%
 
113425.5%
 
2021-03-21T19:14:38.701868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Loan_Purpose
Real number (ℝ≥0)

ZEROS

Distinct count86
Unique (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6974929286437432
Minimum0.0
Maximum3.0
Zeros12
Zeros (%)2.3%
Memory size4.1 KiB
2021-03-21T19:14:38.789035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5524475004
Coefficient of variation (CV)0.3254490732
Kurtosis0.9830081418
Mean1.697492929
Median Absolute Deviation (MAD)0
Skewness-0.54875092
Sum891.1837875
Variance0.3051982406
2021-03-21T19:14:38.880932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
228153.5%
 
112924.6%
 
3214.0%
 
0122.3%
 
1.87465681710.2%
 
1.48735317410.2%
 
1.59697678710.2%
 
1.51704424110.2%
 
1.83357407910.2%
 
1.63719347310.2%
 
Other values (76)7614.5%
 
ValueCountFrequency (%) 
0122.3%
 
112924.6%
 
1.46130628410.2%
 
1.47456888910.2%
 
1.47572694810.2%
 
ValueCountFrequency (%) 
3214.0%
 
2.11064412210.2%
 
228153.5%
 
1.93496256110.2%
 
1.87465681710.2%
 

State
Real number (ℝ≥0)

Distinct count40
Unique (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.432380952380953
Minimum0.0
Maximum39.0
Zeros2
Zeros (%)0.4%
Memory size4.1 KiB
2021-03-21T19:14:38.977216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median14
Q326
95-th percentile37
Maximum39
Range39
Interquartile range (IQR)22

Descriptive statistics

Standard deviation11.78179874
Coefficient of variation (CV)0.716986709
Kurtosis-1.350402243
Mean16.43238095
Median Absolute Deviation (MAD)10
Skewness0.3562846932
Sum8627
Variance138.8107815
2021-03-21T19:14:39.070083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
412924.6%
 
25428.0%
 
8397.4%
 
33356.7%
 
22234.4%
 
9203.8%
 
11203.8%
 
37193.6%
 
26183.4%
 
29183.4%
 
Other values (30)16230.9%
 
ValueCountFrequency (%) 
020.4%
 
120.4%
 
251.0%
 
3163.0%
 
412924.6%
 
ValueCountFrequency (%) 
3930.6%
 
3881.5%
 
37193.6%
 
3610.2%
 
35142.7%
 

Debt_to_Income
Real number (ℝ≥0)

Distinct count472
Unique (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.414971428571429
Minimum0.72
Maximum29.85
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-03-21T19:14:39.168443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.72
5-th percentile4.066
Q19.46
median14.43
Q319.71
95-th percentile24.38
Maximum29.85
Range29.13
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation6.470240378
Coefficient of variation (CV)0.4488555811
Kurtosis-0.7899920857
Mean14.41497143
Median Absolute Deviation (MAD)5.2
Skewness0.01779949874
Sum7567.86
Variance41.86401054
2021-03-21T19:14:39.261312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1030.6%
 
13.2230.6%
 
15.820.4%
 
7.9820.4%
 
5.520.4%
 
11.9320.4%
 
21.0120.4%
 
14.4320.4%
 
18.7920.4%
 
1320.4%
 
Other values (462)50395.8%
 
ValueCountFrequency (%) 
0.7210.2%
 
0.7810.2%
 
0.9410.2%
 
0.9810.2%
 
110.2%
 
ValueCountFrequency (%) 
29.8510.2%
 
29.4410.2%
 
29.0410.2%
 
28.410.2%
 
28.0610.2%
 

Delinquent_2yr
Categorical

Distinct count4
Unique (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0
498
1
 
21
2
 
5
3
 
1
ValueCountFrequency (%) 
049894.9%
 
1214.0%
 
251.0%
 
310.2%
 
2021-03-21T19:14:39.474907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Revolving_Cr_Util
Real number (ℝ≥0)

Distinct count395
Unique (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.88081904761905
Minimum0.1
Maximum99.8
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-03-21T19:14:39.564183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile13.5
Q147.6
median67.2
Q382.3
95-th percentile94.76
Maximum99.8
Range99.7
Interquartile range (IQR)34.7

Descriptive statistics

Standard deviation24.53060893
Coefficient of variation (CV)0.390112745
Kurtosis-0.4375928482
Mean62.88081905
Median Absolute Deviation (MAD)16.5
Skewness-0.6481571639
Sum33012.43
Variance601.7507743
2021-03-21T19:14:39.643161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
74.961.1%
 
55.540.8%
 
83.740.8%
 
91.240.8%
 
82.930.6%
 
79.530.6%
 
90.830.6%
 
83.530.6%
 
6530.6%
 
33.330.6%
 
Other values (385)48993.1%
 
ValueCountFrequency (%) 
0.110.2%
 
0.610.2%
 
1.410.2%
 
3.220.4%
 
410.2%
 
ValueCountFrequency (%) 
99.810.2%
 
99.310.2%
 
9920.4%
 
98.510.2%
 
98.110.2%
 

Total_Accounts
Real number (ℝ≥0)

Distinct count48
Unique (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.98857142857143
Minimum3.0
Maximum79.0
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-03-21T19:14:39.729523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q113
median18
Q326
95-th percentile38
Maximum79
Range76
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.868402369
Coefficient of variation (CV)0.493702234
Kurtosis2.784835567
Mean19.98857143
Median Absolute Deviation (MAD)6
Skewness1.183716282
Sum10494
Variance97.38536532
2021-03-21T19:14:39.818575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
18315.9%
 
11295.5%
 
13275.1%
 
14275.1%
 
12265.0%
 
15234.4%
 
23224.2%
 
10224.2%
 
19224.2%
 
21214.0%
 
Other values (38)27552.4%
 
ValueCountFrequency (%) 
351.0%
 
420.4%
 
530.6%
 
661.1%
 
7132.5%
 
ValueCountFrequency (%) 
7910.2%
 
6110.2%
 
5610.2%
 
5120.4%
 
5010.2%
 

Longest_Credit_Length
Real number (ℝ≥0)

Distinct count31
Unique (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.03047619047619
Minimum3.0
Maximum40.0
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2021-03-21T19:14:39.919878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q18
median11
Q315
95-th percentile24.8
Maximum40
Range37
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.987823583
Coefficient of variation (CV)0.4977212446
Kurtosis1.929481763
Mean12.03047619
Median Absolute Deviation (MAD)3
Skewness1.266057958
Sum6316
Variance35.85403126
2021-03-21T19:14:40.011011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
116011.4%
 
9499.3%
 
13417.8%
 
10397.4%
 
7356.7%
 
6346.5%
 
5326.1%
 
8305.7%
 
12295.5%
 
15234.4%
 
Other values (21)15329.1%
 
ValueCountFrequency (%) 
340.8%
 
4163.0%
 
5326.1%
 
6346.5%
 
7356.7%
 
ValueCountFrequency (%) 
4010.2%
 
3310.2%
 
3220.4%
 
3120.4%
 
3061.1%
 

Interactions

2021-03-21T19:14:23.945280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:24.641142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:24.776020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:25.106688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:25.236225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:25.365659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:25.504045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:25.655813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:25.802686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:25.933463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:26.061818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:26.193892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:26.325500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:26.445703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:26.556293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:26.663527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:26.775317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:26.882075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:26.988075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:27.100031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:27.211974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:27.312126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:27.410939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:27.508636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:27.621858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:27.775505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:27.892701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:28.003446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:28.116616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:28.246844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:28.364765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:28.495812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:28.623986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:28.853204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:28.975852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:29.115695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:29.241642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:29.359656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:29.477286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:29.604092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:29.737065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:29.852086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:30.031833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-21T19:14:30.559278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:30.662022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:30.782087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:30.908007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:31.038848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:31.168148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:31.288959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:31.415984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:31.539700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:31.665616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:31.787978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:31.913583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:32.043793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:32.172704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:32.296791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:32.419738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:32.634360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:32.752980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:32.870422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:32.991995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:33.109659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:33.216174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:33.322018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:33.425251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:33.527254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:33.690269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:33.783204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:33.881891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:33.982193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:34.083581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:34.181994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-21T19:14:34.802069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:34.920793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:35.048738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-21T19:14:35.559474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:35.688785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-21T19:14:35.947680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:36.077110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-21T19:14:36.744304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:36.849312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:36.960219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-21T19:14:40.117977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-21T19:14:40.641021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-21T19:14:40.846975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-21T19:14:41.055602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-21T19:14:41.307926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-21T19:14:37.271100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-21T19:14:37.540594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

Loan_AmountTermInterest_RateEmployment_YearsHome_OwnershipAnnual_IncomeVerification_StatusLoan_PurposeStateDebt_to_IncomeDelinquent_2yrRevolving_Cr_UtilTotal_AccountsLongest_Credit_Length
05000.036.010.6510.02.024000.00.01.0000003.027.650.083.79.026.0
12500.060.015.270.52.030000.01.00.0000009.01.000.09.44.012.0
22400.036.015.9610.02.012252.02.01.50607811.08.720.098.510.010.0
310000.036.013.4910.02.049200.01.01.8366534.020.000.021.037.015.0
45000.036.07.903.02.036000.01.01.7261403.011.200.028.312.07.0
53000.036.018.649.02.048000.01.00.0000004.05.350.087.54.04.0
65600.060.021.284.01.040000.01.01.7237964.05.550.032.613.07.0
75375.060.012.690.52.015000.00.01.74326333.018.080.036.53.07.0
86500.060.014.655.01.072000.02.02.0000003.016.120.020.623.013.0
912000.036.012.6910.01.075000.01.02.0000004.010.780.067.134.022.0

Last rows

Loan_AmountTermInterest_RateEmployment_YearsHome_OwnershipAnnual_IncomeVerification_StatusLoan_PurposeStateDebt_to_IncomeDelinquent_2yrRevolving_Cr_UtilTotal_AccountsLongest_Credit_Length
5152000.036.09.9110.02.024000.0000002.01.58956111.013.500.064.210.012.0
51616800.036.014.652.02.060000.0000000.01.00000033.014.520.096.914.08.0
5178000.036.08.903.00.0120000.0000002.01.0000004.010.520.060.615.011.0
51812000.060.012.699.00.057000.0000002.02.0000006.019.710.066.313.06.0
51911000.036.07.904.00.080000.0000002.01.0000003.04.740.077.715.012.0
5205000.036.016.296.00.085000.0000001.01.48292337.022.810.090.017.07.0
52118000.036.016.2910.01.065595.0742740.02.00000033.021.800.090.235.011.0
52210000.036.012.696.01.036000.0000000.02.0000004.015.900.065.310.05.0
5235000.036.09.910.52.070000.0000001.01.00000026.013.920.064.323.021.0
52410000.036.016.294.02.051360.0000001.01.00000025.016.610.079.19.04.0